Factors Associated with Physical Activity Increases and Decreases Among a Sample of Appalachian Residents During the COVID-19 Pandemic: A Cross-Sectional Study

Introduction Physical activity (PA) can prevent and reduce the deleterious physical and mental health effects of COVID-19 and associated lockdowns. Research conducted early in the pandemic demonstrates that a greater proportion of adults in the U.S. have decreased than increased PA, and the effects vary by sociodemographic factors. Ongoing evidence is important to identify patterns in PA changes during the pandemic. Purpose This study aims to identify factors associated with increases and decreases in PA during the COVID-19 pandemic in a convenience sample of adults residing in Appalachia. Methods: Surveys were collected from a convenience sample of adults from eight counties in West Virginia from January to March 2021. Logistic regression analysis was used to identify sociodemographic, health, and rurality factors associated with (1) increased PA and (2) decreased PA during the pandemic, assessed retrospectively via self-report. Results Analysis of 1,401 survey responses revealed that better self-rated health, lower body mass index, and higher income and education were associated with a greater likelihood of more time spent doing PA during the pandemic ( p ≤ .05). Respondents with lower self-rated health, higher body mass index, lower income, and lower levels of education—plus females and those living in a more urban county—were more likely to spend less time doing PA during the pandemic ( p ≤ .05). Implications Analyses suggest that pre-pandemic disparities in PA by health, wealth, and education were exacerbated during the pandemic. These must be addressed before physical inactivity and ill health become endemic to the Appalachian Region.


INTRODUCTION
multitude of disease morbidity and mortality indicators are worse in Appalachia than in the rest of the U.S. 1 Nearly all the indicators (e.g., cardiovascular disease, type 2 diabetes) are associated with physical inactivity, 2,3 which is also significantly higher in Appalachia. 1 Sufficient amounts of physical activity (PA)-a behavior with distinct etiology and effects from those of physical inactivity 4 -is essential to the prevention and treatment of these diseases.Notably, research has also linked PA to the reduction of negative physical and mental health impacts of COVID-19.Physical health benefits include reduced susceptibility to and severity of COVID-19 outcomes 5 by strengthening immune function, preventing chronic disease, and reducing inflammation. 6PA is also beneficial in the prevention and treatment of anxiety, depression, and other mental health concerns exacerbated by pandemic isolation. 7PA has demonstrated associations with well-being by helping people experience detachment, relaxation, mastery experiences, and control of leisure time during COVID-19-related lockdowns. 8veral studies evaluating changes in PA among adults in the U.S. during the early months of the pandemic demonstrate that PA declined in spring and summer 2020 immediately after the pandemic was declared [9][10][11] and remained at the reduced level in spring and early summer 2021. 12Dunton et al.'s analyses of survey data 9 collected between April 19 and May 25, 2020, from 268 respondents (58% of whom lived in California) demonstrated significant decreases of 60-90 minutes per week of moderate-intensity PA, vigorousintensity PA, and walking.Self-report data from 1,809 respondents (65.6% residing in Texas) collected from April 15 to May 5, 2020, demonstrated that more respondents decreased their PA than increased (39.0% and 25.2%, respectively) since the pandemic began. 10Using a similar single-item assessment of PA change among 3,829 U.S. respondents to national consumer panel surveys from March 19 to April 9, 2020, and June 10 to 25, 2020, Watson et al. 11 also found that more respondents reported PA decreases than increases (30.4% and 20.3%, respectively).The pattern of findings was supported by analysis of step count data from smartphones in a subset of 143 of the respondents in the Dunton et al. study 9 that showed an average reduction of 2,232.4steps per day (April 15 to May 5, 2020), and from the 10 U.S. cities in Tison et al.'s global study 13 that showed roughly 20-40% decreases in daily step counts in mid-April from baseline (February).
There have been sociodemographic and geographic disparities in COVID-19related PA changes.For instance, Dunton et al. 9 discovered greater decreases in A self-reported minutes of walking from April 15 to May 5, 2020, for participants who were Hispanic and low-income, while Watson et al. 11 found that reporting being more active from March/April to June 2020 was associated with being female, aged 18-44 years, White, not obese, educated at or above college level, residing in urban areas and in the Midwest region.Knell et al. 10 confirmed the finding that females were more likely to increase PA and found that people with children in the home were more likely to increase PA from April 15 to May 5, 2020.These results align mostly with pre-pandemic PA trends, 14 apart from the disparity between the sexes.Pre-existing disparities in access to safe places to be active by rural-urban status 15 and by socioeconomic status 16 may have exacerbated the pandemic-related PA changes.Surveys conducted during the pandemic reveal that at least half of the respondents are active at home or on roads/sidewalks in their neighborhoods. 9,11Home exercise equipment and walkable neighborhood spaces are less common in low-income, rural areas 15

like much of Appalachia.
There is also evidence from 10 cities in the U.S. 13 that PA declines witnessed during the early, most restrictive lockdown phases of the pandemic (February to mid-April, 2020), may abate toward pre-pandemic levels over time, but may lag behind the easing of COVID-19-related policy restrictions. 12To date, there is no evidence of PA changes during the COVID-19 pandemic experienced by residents of Appalachia.To better our understanding of the impact of the pandemic on PA over time in the U.S., ongoing research evaluating pandemic-related PA changes and disparities by geography and sociodemographic characteristics is needed.Thus, this study assesses the sociodemographic and geographic characteristics associated with increases and decreases in PA during the pandemic in a sample of Appalachian adults using a retrospective, cross-sectional survey.

Data Collection
Data for this study were collected from January to March 2021 as part of the community input process of the Community Health Needs Assessments (CHNAs) for two non-profit hospitals in West Virginia (WV) with service areas covering eight contiguous counties in Western WV (see Table 1).The community served was defined by the CHNA leadership team for Hospital 2 (H2) as only one county (Jackson), whereas the team for Hospital 1 (H1) chose the seven other counties that are listed in Table 1.As an Internal Revenue Service requirement, at least every three years all non-profit hospitals in the U.S. are required to conduct a CHNA to inform decision-making.CHNAs provide a unique opportunity to collect health behavior and health status data to overcome the lack of time-sensitive data in areas of limited population that are often underserved by national surveillance data.
A convenience sample of survey responses was collected using a snowball sampling technique.Hospital staff and community partners were provided an anonymous link to an online survey 17 and asked to share the link with personal and professional contacts by email.Additionally, H1 utilized the inbox/messaging capabilities within their electronic medical records system to send the survey link to patients within their seven-county service area and shared it via their social media accounts.Both hospitals also worked with community partners (e.g., United Way, public libraries) to disseminate paper copies of the survey to capture responses from populations under-represented in electronic health records and online surveys.Completed paper surveys were returned to the research team for input via the anonymous link.Individuals under age 18 years or residing outside a county served by the hospital were excluded from completing a survey.No incentives were provided to survey respondents.The study was approved for protection of human subjects by the West Virginia University Institutional Review Board (protocol no.2003942005).

Measures
The surveys included sections of items about community health perceptions and priorities, healthcare access, individual health behaviors, and COVID-19-related health behavior changes.

Dependent Variables
Two dependent variables were used in analyses: (1) increased PA during the pandemic and (2) decreased PA during the pandemic.Survey respondents were asked, "In the past six months, which of the following are things you have done in response to the coronavirus pandemic?"Respondents selected all that applied from a list of 24 response options, with 23 pandemic-related behaviors and a "None of the above" response option.The multiselect item technique and the list of pandemic-related impacts is an amalgam of what has been used in national studies in the U.S. (i.e., the COVID-19 Participant Experience Survey [COPE] and the COVID Impact Survey). 18,19In effect, each item in the list is treated as separate survey items with a binary response: "yes" if checked and "no" if not checked.Thus, there were 23 items, two of which were utilized for dependent variables: "Spent more time doing physical activity" and "Spent less time doing physical activity."If a respondent selected "Spent more time doing physical activity," that response was coded as 1 for "yes" for increased PA.Similarly, if the respondent selected "Spent less time doing physical activity", that response was coded as 1 for "yes" for decreased PA.The method for determining the "no" response for each of the dependent variables required a review of the full set of 24 response options to determine whether the respondent skipped the question or truly did not select the item (i.e., a "no" response).For each dependent variable, if the respondent did not select the PA option but did select at least one of the other pandemicrelated behaviors (i.e., did not skip the question), that response was coded as 0 ("no") for the dependent variable.To be conservative, if respondents selected both PA options (i.e., both increased and decreased PA in response to the pandemic); none of the 24 items; or "None of the above", then those responses were coded as missing for both dependent variables and excluded from analyses.
A state of emergency was in place in WV during the entire data collection period, inclusive of the item-response recall period of "in the past 6 months" (i.e., July 2020 to March 2021).COVID-19 restrictions in place or removed are noted as follows based on the Governor's executive orders. 20On July 1, 2020, several outdoor activities were permitted to resume, including fairs, festivals, amusement parks, and outdoor open-air concerts.However, this was rescinded two weeks later, and a limit of social gatherings to 25 people was put in place due to a spike in COVID-19 cases.In September, that was reduced to 10 based on the degree of community spread of COVID-19, which was tracked weekly using a color-coded system that also determined whether in-person K-12 instruction and scholastic sports could occur.From September 8, 2020, until the December holidays break, school systems varied in delivery based on parents' choice to send their child to school full-time in person, virtually, or two days per week in person ("hybrid").The first vaccinations occurred on December 14, 2020.The majority of K-12 school systems (48 of 55) returned to in person instruction 5 days per week on January 19, 2021, but winter sports were not allowed to resume until March.Outdoor fairs, festivals, and recreation-related businesses were allowed to reopen, and restaurants and bars were allowed to increase capacity from 50% to 75% in late February.On March 5, 2021, restaurants and bars were allowed to increase to full capacity and K-12 winter sports were allowed to resume unless an outbreak was indicated using the colorcoded tracking system.On March 24, 2021, the use of the color-coded tracking system ended for all schools and related activities, and all live, indoor music performances were allowed to resume.

Independent Variables
Independent sociodemographic variables included age (18-44, 45-64, ≥65 years  [referent]), self-rated health (good/excellent, very poor/poor/fair [referent]), body mass index (BMI) using self-reported height and weight (underweight/normal weight, overweight, obese [referent]), sex (male, female [referent]), marital status (married/in a domestic partnership, all others [referent]), ethnicity (not Hispanic or Latino, Hispanic or Latino), race (White, all other races [referent]), yearly household income ( ≥$75,000; $50,000 to $74,999; $30,000 to $49,999; < $30,000 [referent]), education (bachelor's degree or higher, completed some postsecondary education, ≤ High school diploma or equivalent (GED) [referent]), and children in the home (none, one or more [referent]).County-level rurality (mostly urban, mostly rural, completely rural [referent]) was determined by coding each respondent's self-reported county of residence based on the percentage of a county's population not living in an urban area (see Table 1) according to 2010 Census data. 21Counties with <50% of the population living in rural areas are classified as mostly urban; 50 to 99.9% as mostly rural; 100% rural as completely rural.Previous research has shown variation in county-level environmental factors associated with adults' PA along the rural-urban continuum when using this variable. 22County-level rural-urban categorizations based on population and commuting data (e.g., Rural-Urban Continuum Codes, Urban Influence Codes) were not used, as commuting was severely restricted during the data collection period due to the COVID-19 restrictions.

Statistical Methods
Analyses were conducted using SAS 9.4©, Cary, NC. 23 Sixty-one responses were missing on the dependent variables (3.1%) for the following reasons: 37 respondents selected both PA options; 13 selected none of the 24 items; and 11 selected "None of the above."Due to the small percentage (<5%) missing on the dependent variables, data were treated with the default pairwise deletion, which uses all valid pairs of data and provides valid inference if data are missing at random (MAR) or missing completely at random (MCAR).Independent variables included income, a known sensitive item with a higher percent of non-response.However, this would be considered MAR data for the dependent variables (i.e., data are missing due to a predictor variable, not because of the outcome).The primary concern for reduction in sample size for the final logistic regression model (from n = 1951 to n = 1401, 28.2%) would be power, which was maintained for the effects found in this analysis.
Descriptive statistics included frequencies and valid percentages for all categorical variables.Logistic regression was used to model odds ratios (ORs) with 95% confidence limits (CLs) for each independent variable separately, and adjusted odds ratios (AORs) with 95% CLs for each independent variable when adjusted for all other independent variables.Data were assessed for violation(s) of the assumptions of logistic regression.Ethnicity was removed as an independent variable for violating the assumption of sufficient cell size.

RESULTS
A total of 1,951 surveys met the sampling frame criteria, including 1,765 from H1 and 186 from H2.The majority of the respondents were White (n = 1,872, 97.6%), female (n = 1,289, 67.3%), married or in a domestic partnership (n = 1,323, 68.5%), residing in a mostly urban county (n = 1,425, 73.0%), and had good or excellent self-reported health (n = 1,155, 59.5%).Almost half of the respondents were classified as obese (n = 899, 49.0%).Compared with the county profiles (Table 1), survey respondents tended to be older, have more education and higher income, and over-represented in comparison to females.

DISCUSSION
The survey data reported herein provide an initial understanding of the sociodemographic factors associated with PA increases and PA decreases during the COVID-19 pandemic among a sample of adults in Appalachia.Assessed in early 2021, results highlight the ongoing negative implications of the pandemic that reinforces and extends the findings of other studies conducted earlier in the pandemic in other areas of the U.S. Other studies conducted in March and June 2020, using similar items directly asking if PA increased, decreased, or stayed the same, revealed a similar percentage of respondents decreasing PA (30.4%) but a greater percentage increasing PA (20.3%) 11 than observed here.Knell et al. 10 reported a higher rate of both decreasing PA (39.0%) and increasing PA (25.2%), with surveys conducted in April/May 2020 using a continuous scale of PA.6][27] Further, Appalachian adults had higher prevalence of sedentarism (i.e., lack of leisure-time PA) prior to the pandemic. 1Decreasing the already low levels of PA in Appalachia, as the findings suggest is happening, could exacerbate the significant mental and physical health disparities in Appalachia that predated the pandemic. 1Significant investments of human, organizational, and fiscal capital; considering PA in all policy decisions; and increasing access to PA for Appalachian residents of all ages and abilities are potential solutions to sedentarism and ill health before they become endemic to the region.
In the adjusted models, factors associated with spending less time doing PA included living in a more urban area; being female; having obesity; having a lower household income; and reporting very poor, poor, or fair health.These populations should be prioritized for future interventions in Appalachia.The findings support those of Dunton et al. 9 , who found greater decreases in selfreported minutes of walking for respondents with low income, and those of Watson et al. 11 , who found that urban residence was associated with less PA during the pandemic.It's likely that urban residents depended more on indoor PA at fitness facilities that were shuttered during the pandemic and/or had limited access to outdoor places for PA.Rural residents may also have been subject to fewer, shorter, and/or less stringently enforced COVID-19-related restriction policies, a factor associated with PA. 12 It should be noted that (1) our county-level definition of rural differed from that used in similar research by Watson et al. 11 and (2) the county where our mostly urban respondents resided had 26.8% of residents living outside an urban area, higher than national data (11.0%). 28 in Watson et al. 11 , in this study's adjusted models, spending more time doing PA during the pandemic was associated with not having obesity and with having earned a bachelor's degree or higher, though the Appalachian Region has higher obesity rates and a lower rate of bachelor's degree attainment than the rest of the U.S. 1 In addition, having greater self-rated health and having a higher annual household income were associated with more time spent doing PA during the pandemic, likely widening pre-pandemic health disparities that have been shown to be predictive of pandemic-related health behaviors in the U.K. 29 and in the U.S. 12 These findings do not support previous studies that found that women were more likely than men to increase PA during the pandemic 10,11 , nor did they concur with Knell et al.'s 10 finding that having a child in the home was associated with increasing PA.Age, urbanicity and race were not significantly associated with increasing PA as they were in Watson et al. 11 , though our sample was older, and very homogeneous with respect to race, ethnicity, and geography, and these analyses adjusted for predictors.

Limitations
The results of this study should be considered in light of multiple limitations.First, these data were collected using a convenience sample from one state that may not have accurately represented the population, limiting generalizability but complementing other regional survey research on COVID-19-related PA changes among adults in the U.S. 9,10 Second, the dependent variables were assessed using a multiselect item similar to other COVID-19-related studies 18,19 though without a "no change" response option, rendering an inability to identify true "no" selections.This was addressed through use of very conservative definitions to remove perceived non-responses.Third, the county-level definition of rurality utilized here was selected because the narrowest geography captured by the survey was county of residence; this limits comparability of study results with other relevant studies, and possibly overrepresents rural residents.Fourth, data collection occurred in winter months (January-March) which may have biased the responses about PA despite survey instructions asking respondents to consider "the past six months" and "in response to the coronavirus pandemic" when considering if they had increased and/or decreased time spent doing PA.The length of time of recall from the start of the pandemic to the survey data collection (9-12 months) may have also biased the responses.

Table 1 . Census profile of counties where CHNA survey data were collected, 2019
NOTES: Data are from the U.S. Census Bureau's American Community Survey ((https://www.census.gov/programssurveys/acs),5-year estimates (2019) except for the rural population percentage (2010 Decennial Census; https://data.census.gov/cedsci/)*Rural population percentage is the percentage of a county's population not living in an urban area, 2010 † Education is educational attainment for population 25 years and older § Married is "now married (except separated)", aged 15 years & older

Table 2 ,
roughly twice as many respondents indicated they spent less time doing PA during the pandemic (n = 584, 30.1%) than more time doing PA (n = 303, 16.0%).Over two-fifths (n = 323, 41.0%) of individuals with selfrated health of very poor, poor, or fair indicated spending less time doing PA during the pandemic.Only two subcategories of respondents more frequently indicated they spent more time doing PA than spent less time doing PA during the pandemic: (1) those reporting under-or normal weight(27.2%v.20.4%) and (2) those with annual household income ≥$75,000 (24.5% v. 24.1%).A full description of the sample is provided in Table2.